Introductory description

This module builds on concepts covered in Year 1 and Year 2 modules and expands knowledge and understanding of molecular simulations, electronic structure calculations and machine learning. The module includes numerous “case studies” to both diversify the content and highlight the usefulness of computational chemistry in research contexts and industrial scenarios.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The module is split into 3 topics that will be delivered via lectures and supported by workshops. Each section will also cover applications and case studies.

1. Molecular simulations
2. Electronic structure calculations
3. Machine learning

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate understanding of the theoretical foundations underpinning the main methods used in modern computational chemistry, including molecular dynamics, electronic structure theory, and machine learning methods.
• Choose appropriate methods to address specific computational modelling problems from various modern application areas.
• Apply computational chemistry methods to calculate measurable molecular and materials properties and analyse the results of simulations.
• Assess the capabilities and limitations of common computational chemistry tools and their relationship to experimental techniques.
• Design and create modelling, simulation, and visualisation workflows via scientific coding and machine learning techniques.

Indicative reading list

Reading lists can be found in Talis

Specific reading list for the module

Subject specific skills

Design and create modelling, simulation, and visualisation workflows via scientific coding and machine learning techniques.

Transferable skills

Communication
Critical Thinking
Digital Literacy
Information Literacy
Problem Solving
Professionalism